Internet Quarantine: Requirements for Containing Self-Propagating Code

David Moore et. al.University of California, San Diego

Internet Quarantine: Requirements for Containing Self-Propagating Code

Aleksandar KuzmanovicRice University, COMP 629

Outline

Background about worm, esp. Code-Red– What’s worm, esp. Code-Red– Prevention, Treatment and Containment of the worm.

SI epidemic model and Code Red propagation model.

Simulations on Code Red Propagation and Containment System Deployment.

Conclusion.

Background: what is worm?

Worm is a self-replicating software designed to spread through the network.

Worm vs Virus and Trojan horse– Virus and Trojan horse rely on human intervention

to spread.– Worm is autonomous.

Background: Code-Red v1

Outbreak: June 18, 2001

How it works:– Buffer overflow exploit on Microsoft IIS web server.– Upon infected a machine, randomly generate a list of IP

addresses.– Probe each of the addresses from the list.

Payload: DDoS attack against www1.whitehouse.gov.

Damage: little– Fixed random seed.

Background: Code-Red v2

Outbreak: July 19, 2001

How it works:– Similar to Code-Red v1, but with a random seed.– Generates 11 probes for second.

Damage: severe– 359,000 machines were infected within 14 hours.

How to mitigate the threat of worms(1)

Three approaches– Prevention:

Reduce the size of the vulnerable population. E.g. A single vulnerability in a popular software system

can result in millions of vulnerable hosts. E.g. Code Red attacks millions of MS IIS web server.

How to mitigate the threat of worms (2)

Treatment:– E.g. virus scanner.– The time required to design, develop and test a security

flaw is usually for too slow than the spread of the worm.

Containment:– E.g. firewall, filters– Containment is used to protect individual networks, and

isolate infected hosts.

SI Model (1)

NIS

dtdI

NIS

dtdS

In this work, a vulnerable machine is described as susceptible (S) machine.

A infected machine is described as infected (I). Let N be the number of vulnerable machines. Let S(t) be the number of susceptible host at time t, and s(t) be

S(t)/N, where N = S(t) + I(t). Let I(t) be the number of infected hosts at time t, and i(t) be I(t)/N. Let be the contact rate of the worm. Define:

SI Model (2)

)1(

)()(1

iidtdi

titsNN

SIdtdi

)(

)(

1)( Tt

Tt

eeti

Solving the differential equation:

where T is a constant

Code Red Propagation Model (1)

Code Red generates IPv4 address by random. Thus, there are totally 2^32 addresses.

Let r be the probe rate of a Code Red worm. Thus:

322Nr

Code Red Propagation Model (2)

Two problems– Cannot model preferential targeting algorithm.

E.g. select targets form address ranges closer to the infected host.

– The rate only represents average contact rate. E.g. a particular epidemic may grow significantly more

quickly by making a few lucky targeting decisions in early phase.

Code Red Propagation Model (3)

Example on 100 simulations on Code Red propagation model:

After 4 hours: 55% on average 80% in 95th percentiles 25% in 5th percentiles

Modeling Containment Systems (1)

A containment system has three important properties:

– Reaction time – the time necessary for Detection of malicious activity, Propagation of the containment information to all hosts

participating the system, and Activating any containment strategy.

Modeling Containing Systems (2)

– Containing Strategy

Address blacklisting– Maintain a list of IP addresses that have been identified as

being infected.– Drop all the packets from one of the addresses in the list.– E.g. Mail filter.– Advantage: can be implemented easily with existing

firewall technology.

Modeling Containing Systems (3)

Content filtering– Requires a database of content signatures known to

represent particular worms.– This approach requires additional technology to

automatically create appropriate content signatures.– Advantage: a single update is sufficient to describe any

number of instances of a particular worm implementation.

Deployment scenarios– Ideally, a global deployment is preferable.– Practically, a global deployment is impossible.– May be deploying at the border of ISP networks.

Idealized Deployment (1)

Simulation goal– To find how short the reaction time is necessary to effectively

contain the Code-Red style worm.

Simulation Parameters:– 360,000 vulnerable hosts out of 232 hosts.– Probe rate of a worm : 10 per sec.

Containment strategy implementation– Address blacklisting

Send IP addresses to all participating hosts.– Content filtering

Send signature of the worm to all participating hosts.

Idealized Deployment (2)

Result: content filtering is more effective.

20 min 2 hr

Number ofsusceptiblehost decreases

Wormsunchecked

Idealized Deployment (3)

Next goal:– To find the relationship between containment

effectiveness and worm aggressiveness.– Figures are in log-log scale.

Idealized Deployment (4)

Percentage of infected hosts

Address blacklisting is hopelesswhen encountering aggressive worms.

Practical Deployment (1)

Network Model– AS sets in the Internet:

routing table on July 19,2001 1st day of the Code Red v2 outbreak.

– A set of vulnerable hosts and ASes: Use the hosts infected by Code Red v2 during the initial

24 hours of propagation. A large and well-distributed set of vulnerable hosts.

– 338,652 hosts distributed in 6,378 ASes.

Practical Deployment (2)

Deployment Scenarios– Use content filtering only.– Filtering firewall are deployed on the borders of

both the customer networks, and ISP’s networks.

Deployment of containment strategy.

Practical Deployment (3)

Reaction time: 2hrs

Difference inperformancebecause of thedifference in pathcoverage.

Practical Deployment (4)

System fails to containthe worm.

Conclusion

Explore the properties of the containment system– Reaction time– Containment strategy– Deployment scenario

In order to contain the worm effectively– Require automated and fast methods to detect and react to

worm epidemics.– Content filtering is the most preferable strategy.– Have to cover all the Internet paths when deploying the

containment systems.